{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T14:23:56Z","timestamp":1759674236268},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Power companies such as Southern California Edison (SCE) uses Demand Response (DR) contracts to incentivize consumers to reduce their power consumption during periods when demand forecast exceeds supply. \n\nCurrent mechanisms in use offer contracts to consumers independent of one another, do not take into consideration consumers' heterogeneity in consumption profile or reliability, and fail to achieve high participation. \n\n\n\nWe introduce DR-VCG, a new DR mechanism that offers a flexible set of contracts (which may include the standard SCE contracts) and uses VCG pricing. We prove that DR-VCG elicits truthful bids, incentivizes honest preparation efforts, and enables efficient computation of allocation and prices. With simple fixed-penalty contracts, the optimization goal of the mechanism is an upper bound on probability that the reduction target is missed.\n\n\n\n Extensive simulations show that compared to the current mechanism deployed by SCE, the DR-VCG mechanism achieves higher participation,  increased reliability, and significantly reduced total expenses.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/167","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"1202-1208","source":"Crossref","is-referenced-by-count":6,"title":["Contract Design for Energy Demand Response"],"prefix":"10.24963","author":[{"given":"Reshef","family":"Meir","sequence":"first","affiliation":[{"name":"Technion - Israel Institute of Technology, Faculty of Industrial Engineering and Management"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyao","family":"Ma","sequence":"additional","affiliation":[{"name":"Harvard University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentin","family":"Robu","sequence":"additional","affiliation":[{"name":"Heriot-Watt University, Edinburgh"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"acronym":"IJCAI-2017","name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","start":{"date-parts":[[2017,8,19]]},"theme":"Artificial Intelligence","location":"Melbourne, Australia","end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:52:35Z","timestamp":1501242755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/167"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/167","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}